Journal of Engineering Research
Innovation and Scientific Development

Study Pal: An Automated Tool for Supporting Self-Regulated Learning Habits

Document Type : Research Paper

Authors
1F. O. Oliha,  2K.O. Otokiti,  3D.N. Idehen,  4I.E. Obayagbona,  5I.G. Evbuomwan, 
  1. 1  Computer and Information Science Department, Pan Atlantic University, Lagos, Nigeria
  2. 2  Computer Science Department, University of Benin, Nigeria
  3. 3  Computer Science Department, University of Benin, Nigeria
  4. 4  Computer Science Department, University of Benin, Nigeria
  5. 5  Computer Science Department, University of Benin, Nigeria
Abstract

In an academic setting, the development of self-learning or self-study solutions is saddled with several challenges due to poor implementation of model-based self-regulated learning (SRL) strategies for the effective management of students' learning habits. With a case-based approach, the present study developed an SRL solution - Study Pal; a mobile application designed to regulate students' study habits by outlining various components of the Zimmerman cyclic model in the forethought, performance, and reflection stages. The study presented descriptions of each of the phases with respect to their evaluations via a semi-automated tool – TestRail. Results showed that the Study Pal solution is very effective at enhancing student study habits through SRL strategies; guiding students in planning, executing, and reflecting on their study activities to develop good academic performance and higher self-regulation with a success rate peaking at 92%. While the study contributes to existing knowledge by promoting the development of self-directed systems with strategies aligning to the phases of a standard SRL model, it was also evident that the reflective analytics suggest that reflective features of the Study Pal solution could benefit from additional features to improve the overall success rate.

Graphic Abstract
JERISD PUBLICATION LOGO
Vol 2, Number 3
October 2024
Pages 1-8
Files
Download: PDF
History
  • Received: 01/07/2024

  • Revised: 08/09/2024

  • Accepted: 02/10/2024

  • Published: 21/10/2024
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